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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rahul Kumar, Avadhesh Sagar, Shraddha Sharma, Vishnu Galgotias University School of Computing Science and Engineering Greater Noida India ITS Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350356816

Affective computing (AC), also known as emotion AI, is a concept that applies modern technology to help systems to recognize and process human emotions and their feelings. Several uses of affective computing have been demonstrated, including marketing, healthcare, driving assistance, and education. Linear Support vector machines (SVM), k-nearest neighbor (k-NN), Hidden Markov models (HMMs), Gaussian mixture models (GMM), discriminate classifiers (LDC), decision tree algorithms and artificial neural networks (ANN), are the classifiers that are most frequently used in research on identifying and categorizing human emotions. Selecting the right classifier can significantly increase the system's overall efficiency, as numerous studies have conclusively demonstrated. Through use of cameras, microphones, skin sensors, or other devices, affective computing gathers signals from human users, gathering data on their facial expressions, voice tones, gestures, and other characteristics that can hint at their emotional states. The system deduces the user's emotional state from those data points. This study emphasizes on the fundamentals of computing emotions. © 2024 IEEE.

关键词： Support vector machines

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From Posts to Personas: Classifying Users in the Social Media Landscape 5

From Posts to Personas: Classifying Users in the Social Medi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Lekshmi S. Cheriyan, Jo Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam India

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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A Novel Attention-Based Method for Integrating Facial and Speech Features in Emotion Recognition 2

A Novel Attention-Based Method for Integrating Facial and Sp...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350388916

Our research introduces a novel approach for recognizing multiple emotions by leveraging perspective-based methods. This technique integrates facial features and speech characteristics obtained from different encoders to pinpoint the most significant attributes. The distinct characteristics of speech and facial expressions, along with effective size management, prove to be highly beneficial. By concentrating on specific components, the system enhances overall accuracy. Images of facial features are captured from both low and high angles to ensure a comprehensive representation. These techniques are merged through a fusion mesh, producing several feature vectors that are subsequently utilized in the classification stage for sensitivity analysis. When tested with both data sets, this algorithm surpassed existing models, achieving a weighted accuracy (WA) of 90.3%. © 2024 IEEE.

关键词： Attention-Based Method Emotion Classification Facial and Speech Features Fusion Network Multimodal Emotion Recognition Weighted Accuracy

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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Comprehensive Web Analysis of Automobile Websites: Performance, SEO, and Accessibility Insights

Comprehensive Web Analysis of Automobile Websites: Performan...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Busetty, Abhishek Balam, Ruchith Chillakuru, Hari Radha, R. Saranya Devi, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331512965

The current research work proposes a difficultydriven comparison of three most visited automobile websites namely the Automobile Site1, Automobile Site2, Automobile Site3 on parameters including performance, Search engine optimization (SEO), accessibility and traffic. The research involves tool such as Yellow Labs, Google Page Speed Insight and Semrush with regard to the website's weight, loading time, user's interactions and search engine optimization. The findings reveal distinct strengths across the sites: From the three Automobile Site1 is the most optimized and accessibly friendly site, the factors of search engine friendliness is as follows. Looking at the traffic and users of Automobile Site2, it comfortably outclasses the other sites in terms of page views as well as the audience loyalty. At the same time, Automobile Site3 has relatively low first load time which indicates fast website browsing while it is still relatively weak in terms of its SEO and the usability of the site has to be improved in terms of theoretical potential. In conclusion, the realized comparison exposes the strengths and weaknesses of each site and is informative to future developments of the sites with an aim at better user interaction levels and competitive advantage. © 2025 IEEE.

关键词： Automobiles

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VTensor:Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework

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Journal of computer science & Technology 2023年第5期38卷 1074-1097页

作者：俞峰赵家程崔慧敏冯晓兵薛京灵 Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China School of Computer Science and Engineering University of New South WalesSydney 1466Australia

Tensors are a popular programming interface for developing artificial intelligence(AI)*** refers to the order of placing tensor data in the memory and will affect performance by affecting data locality;therefore the deep neural network library has a convention on the *** AI applications can use arbitrary layouts,and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries,operator developers need to write a lot of layout-related code,which reduces the efficiency of integrating new libraries or developing new ***,the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout,thus losing the opportunity for layout *** on the idea of polymorphism,we propose a layout-agnostic virtual tensor programming interface,namely the VTensor framework,which enables developers to write new operators without caring about the underlying physical layout of *** addition,the VTensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors,and runtime layout-oriented optimizations to globally minimize the number of layout transformation *** results demonstrate that with VTensor,developers can avoid writing layout-dependent *** with TensorFlow,for the 16 operations used in 12 popular networks,VTensor can reduce the lines of code(LOC)of writing a new operation by 47.82%on average,and improve the overall performance by 18.65%on average.

关键词： artificial intelligence(AI)programming layout-oblivious tensor processing

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm Optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 IEEE.

关键词： Optimization algorithms

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Voice over Internet Protocol: Custom SIP Server with QoS Optimization for Real-Time Communication 3

Voice over Internet Protocol: Custom SIP Server with QoS Opt...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dinesh Krishna, B.V. Yogendrasai, P. Rajeev, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

VoIP is a revolutionary technology for transmission of voice over internet protocol than from plain telephony systems. But current VoIP solutions have some important issues which are packet loss, latency, and jitter especially when the network is in motion. This work outlines the design, implementation and performance of a locally developed VoIP system which includes a SIP Server Node, Sender Node and a Receiver Node. The SIP Server Node was designed nearly from the ground up to handle session initiation, packet forwarding, and adaptive QoS control. Both Sender and Receiver Nodes had facilities to record, compress and playback audio. The system’s performance was evaluated in a LAN environment, achieving significant improvements in Quality of Service (QoS) metrics: achieving low latency under 150 ms, 95% packet recovery and achieving a good audio quality with the MOS of 4.2. Evaluating the obtained results, it can be concluded that the proposed system is efficient in handling the issues of the packet-switched network and providing high-quality real time voice communication. © 2025 IEEE.

关键词： Packet loss

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Integration of Traffic Shaping with Time-Sensitive Networking for Effective Traffic Management 3

Integration of Traffic Shaping with Time-Sensitive Networkin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Penumada, Archana Reddy Gundapaneni, Divya Jyothi Chennupati, Akshitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Time-Sensitive Networking (TSN) has recently become an important standard to support time-sensitive reliable low-latency data transmission in industrial and real-time applications. This work explores the use of different traffic shaping algorithms, namely, credit-based shaping (CBS) and time-aware shaping (TAS) alongside frame preemption—a TSN instance which gives precedence to high-importance frames by means of lower-priority traffic interruption—to extend TSN’s traffic management capabilities. By controlling data flow and allowing for high priority packets to pass through unimpeded, traffic shaping algorithms serve an essential function in making the network more efficient and reducing congestion rates. The work compares these algorithms in isolation and also in combination over a network model, comprising of four switches and three end users for each switch, using the OMNeT++ simulation environment. This study aims to contribute insights into optimal shaping techniques for TSN, enhancing its suitability for critical network applications. © 2025 IEEE.

关键词： Traffic congestion

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Enhancing Underwater Image Captioning Using Transformer Models and Augmented Terrestrial Datasets

Enhancing Underwater Image Captioning Using Transformer Mode...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gamidi, Rohan Hemasri, M. Muppala, Tejaswi Vinitha Chowdary, A. Aiswariya Milan, K. Palaniswamy, Suja Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508685

Underwater environments present unique challenges for image analysis due to light attenuation, scattering, and color distortion, which significantly degrade image quality. These facts make simple operations such as image captioning, which is crucial in marine sciences, search, and conservation, challenging. Current datasets such as Flickr8K contain mostly terrestrial images and do not possess features distinctive to underwater environments therefore direct training of models is not possible in this context. To cover this gap, the current work combines a Transformer-based image captioning model with an enhanced underwater-specific image augmentation set. These augmentations mimic real-life underwater consequences such as RGB attenuation, Gaussian blur, muddy particles, noise, and gradient-based illumination distortions. The implemented model, learned and recursively tuned on the Flickr8k dataset, proves the ability to generate reasonable captions for underwater images even though the dataset belongs to Flickr8k providing a terrestrial environment. This is proven by the enhanced BLEU scores as well as from the results of the experiment which trained the translation model in less than ten percent of the dataset to show its flexibility on underwater scenarios but without necessitating the use of an underwater dataset. © 2025 IEEE.

关键词： Underwater imaging

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